Natural language processing

Beginning with the basic issues of NLP, this chapter aims to chart the major research activities in this area since the last ARIST Chapter in 1996 (Haas, 1996), including: (i) natural language text processing systems - text summarization, information extraction, information retrieval, etc., including domain-specific applications; (ii) natural language interfaces; (iii) NLP in the context of www and digital libraries ; and (iv) evaluation of NLP systems

A Spoken Dialogue System for Enabling Comfortable Information Acquisition and Consumption

早大学位記番号:新8137早稲田大

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

Inter-relaão das técnicas Term Extration e Query Expansion aplicadas na recuperação de documentos textuais

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-graduação em Engenharia e Gestão do ConhecimentoConforme Sighal (2006) as pessoas reconhecem a importância do armazenamento e busca da informação e, com o advento dos computadores, tornou-se possível o armazenamento de grandes quantidades dela em bases de dados. Em conseqüência, catalogar a informação destas bases tornou-se imprescindível. Nesse contexto, o campo da Recuperação da Informação, surgiu na década de 50, com a finalidade de promover a construção de ferramentas computacionais que permitissem aos usuários utilizar de maneira mais eficiente essas bases de dados. O principal objetivo da presente pesquisa é desenvolver um Modelo Computacional que possibilite a recuperação de documentos textuais ordenados pela similaridade semântica, baseado na intersecção das técnicas de Term Extration e Query Expansion

Advanced techniques for personalized, interactive question answering

Using a computer to answer questions has been a human dream since the beginning of the digital era. A first step towards the achievement of such an ambitious goal is to deal with naturallangilage to enable the computer to understand what its user asks. The discipline that studies the conD:ection between natural language and the represen~ tation of its meaning via computational models is computational linguistics. According to such discipline, Question Answering can be defined as the task that, given a question formulated in natural language, aims at finding one or more concise answers in the form of sentences or phrases. Question Answering can be interpreted as a sub-discipline of information retrieval with the added challenge of applying sophisticated techniques to identify the complex syntactic and semantic relationships present in text. Although it is widely accepted that Question Answering represents a step beyond standard infomiation retrieval, allowing a more sophisticated and satisfactory response to the user's information needs, it still shares a series of unsolved issues with the latter. First, in most state-of-the-art Question Answering systems, the results are created independently of the questioner's characteristics, goals and needs. This is a serious limitation in several cases: for instance, a primary school child and a History student may need different answers to the questlon: When did, the Middle Ages begin? Moreover, users often issue queries not as standalone but in the context of a wider information need, for instance when researching a specific topic. Although it has recently been proposed that providing Question Answering systems with dialogue interfaces would encourage and accommodate the submission of multiple related questions and handle the user's requests for clarification, interactive Question Answering is still at its early stages: Furthermore, an i~sue which still remains open in current Question Answering is that of efficiently answering complex questions, such as those invoking definitions and descriptions (e.g. What is a metaphor?). Indeed, it is difficult to design criteria to assess the correctness of answers to such complex questions. .. These are the central research problems addressed by this thesis, and are solved as follows. An in-depth study on complex Question Answering led to the development of classifiers for complex answers. These exploit a variety of lexical, syntactic and shallow semantic features to perform textual classification using tree-~ernel functions for Support Vector Machines. The issue of personalization is solved by the integration of a User Modelling corn': ponent within the the Question Answering model. The User Model is able to filter and fe-rank results based on the user's reading level and interests. The issue ofinteractivity is approached by the development of a dialogue model and a dialogue manager suitable for open-domain interactive Question Answering. The utility of such model is corroborated by the integration of an interactive interface to allow reference resolution and follow-up conversation into the core Question Answerin,g system and by its evaluation. Finally, the models of personalized and interactive Question Answering are integrated in a comprehensive framework forming a unified model for future Question Answering research

A text mining approach for Arabic question answering systems

As most of the electronic information available nowadays on the web is stored as text,developing Question Answering systems (QAS) has been the focus of many individualresearchers and organizations. Relatively, few studies have been produced for extractinganswers to “why” and “how to” questions. One reason for this negligence is that when goingbeyond sentence boundaries, deriving text structure is a very time-consuming and complexprocess. This thesis explores a new strategy for dealing with the exponentially large spaceissue associated with the text derivation task. To our knowledge, to date there are no systemsthat have attempted to addressing such type of questions for the Arabic language.We have proposed two analytical models; the first one is the Pattern Recognizer whichemploys a set of approximately 900 linguistic patterns targeting relationships that hold withinsentences. This model is enhanced with three independent algorithms to discover thecausal/explanatory role indicated by the justification particles. The second model is the TextParser which is approaching text from a discourse perspective in the framework of RhetoricalStructure Theory (RST). This model is meant to break away from the sentence limit. TheText Parser model is built on top of the output produced by the Pattern Recognizer andincorporates a set of heuristics scores to produce the most suitable structure representing thewhole text.The two models are combined together in a way to allow for the development of an ArabicQAS to deal with “why” and “how to” questions. The Pattern Recognizer model achieved anoverall recall of 81% and a precision of 78%. On the other hand, our question answeringsystem was able to find the correct answer for 68% of the test questions. Our results revealthat the justification particles play a key role in indicating intrasentential relations

Filtrage et agrégation d'informations vitales relatives à des entités

Nowadays, knowledge bases such as Wikipedia and DBpedia are the main sources to access information on a wide variety of entities (an entity is a thing that can be distinctly identified such a person, an organization, a product, an event, etc.). However, the update of these sources with new information related to a given entity is done manually by contributors with a significant latency time particularly if that entity is not popular. A system that analyzes documents when published on the Web to filter important information about entities will probably accelerate the update of these knowledge bases. In this thesis, we are interested in filtering timely and relevant information, called vital information, concerning the entities. We aim at answering the following two issues: (1) How to detect if a document is vital (i.e., it provides timely relevant information) to an entity? and (2) How to extract vital information from these documents to build a temporal summary about the entity that can be seen as a reference for updating the corresponding knowledge base entry?Regarding the first issue, we proposed two methods. The first proposal is fully supervised. It is based on a vitality language model. The second proposal measures the freshness of temporal expressions in a document to decide its vitality. Concerning the second issue, we proposed a method that selects the sentences based on the presence of triggers words automatically retrieved from the knowledge already represented in the knowledge base (such as the description of similar entities).We carried out our experiments on the TREC Stream corpus 2013 and 2014 with 1.2 billion documents and different types of entities (persons, organizations, facilities and events). For vital documents filtering approaches, we conducted our experiments in the context of the task "knowledge Base Acceleration (KBA)" for the years 2013 and 2014. Our method based on leveraging the temporal expressions in the document obtained good results outperforming the best participant system in the task KBA 2013. In addition, we showed the importance of our generated temporal summaries to accelerate the update of knowledge bases.Aujourd'hui, les bases de connaissances telles que Wikipedia et DBpedia représentent les sources principales pour accéder aux informations disponibles sur une grande variété d'entités (une entité est une chose qui peut être distinctement identifiée par exemple une personne, une organisation, un produit, un événement, etc.). Cependant, la mise à jour de ces sources avec des informations nouvelles en rapport avec une entité donnée se fait manuellement par des contributeurs et avec un temps de latence important en particulier si cette entité n'est pas populaire. Concevoir un système qui analyse les documents dès leur publication sur le Web pour filtrer les informations importantes relatives à des entités pourra sans doute accélérer la mise à jour de ces bases de connaissances. Dans cette thèse, nous nous intéressons au filtrage d'informations pertinentes et nouvelles, appelées vitales, relatives à des entités. Ces travaux rentrent dans le cadre de la recherche d'information mais visent aussi à enrichir les techniques d'ingénierie de connaissances en aidant à la sélection des informations à traiter. Nous souhaitons répondre principalement aux deux problématiques suivantes: (1) Comment détecter si un document est vital (c.à.d qu'il apporte une information pertinente et nouvelle) par rapport à une entité donnée? et (2) Comment extraire les informations vitales à partir de ces documents qui serviront comme référence pour mettre à jour des bases de connaissances? Concernant la première problématique, nous avons proposé deux méthodes. La première proposition est totalement supervisée. Elle se base sur un modèle de langue de vitalité. La deuxième proposition mesure la fraîcheur des expressions temporelles contenues dans un document afin de décider de sa vitalité. En ce qui concerne la deuxième problématique relative à l'extraction d'informations vitales à partir des documents vitaux, nous avons proposé une méthode qui sélectionne les phrases comportant potentiellement ces informations vitales, en nous basant sur la présence de mots déclencheurs récupérés automatiquement à partir de la connaissance déjà représentée dans la base de connaissances (comme la description d'entités similaires).L'évaluation des approches proposées a été effectuée dans le cadre de la campagne d'évaluation internationale TREC sur une collection de 1.2 milliard de documents avec différents types d'entités (personnes, organisations, établissements et événements). Pour les approches de filtrage de documents vitaux, nous avons mené nos expérimentations dans le cadre de la tâche "Knwoledge Base Acceleration (KBA)" pour les années 2013 et 2014. L'exploitation des expressions temporelles dans le document a permis d'obtenir de bons résultats dépassant le meilleur système proposé dans la tâche KBA 2013. Pour évaluer les contributions concernant l'extraction des informations vitales relatives à des entités, nous nous sommes basés sur le cadre expérimental de la tâche "Temporal Summarization (TS)". Nous avons montré que notre approche permet de minimiser le temps de latence des mises à jour de bases de connaissances

Assessing relevance using automatically translated documents for cross-language information retrieval

This thesis focuses on the Relevance Feedback (RF) process, and the scenario considered is that of a Portuguese-English Cross-Language Information Retrieval (CUR) system. CUR deals with the retrieval of documents in one natural language in response to a query expressed in another language. RF is an automatic process for query reformulation. The idea behind it is that users are unlikely to produce perfect queries, especially if given just one attempt.The process aims at improving the queryspecification, which will lead to more relevant documents being retrieved. The method consists of asking the user to analyse an initial sample of documents retrieved in response to a query and judge them for relevance. In that context, two main questions were posed. The first one relates to the user's ability in assessing the relevance of texts in a foreign language, texts hand translated into their language and texts automatically translated into their language. The second question concerns the relationship between the accuracy of the participant's judgements and the improvement achieved through the RF process. In order to answer those questions, this work performed an experiment in which Portuguese speakers were asked to judge the relevance of English documents, documents hand-translated to Portuguese, and documents automatically translated to Portuguese. The results show that machine translation is as effective as hand translation in aiding users to assess relevance. In addition, the impact of misjudged documents on the performance of RF is overall just moderate, and varies greatly for different query topics. This work advances the existing research on RF by considering a CUR scenario and carrying out user experiments, which analyse aspects of RF and CUR that remained unexplored until now. The contributions of this work also include: the investigation of CUR using a new language pair; the design and implementation of a stemming algorithm for Portuguese; and the carrying out of several experiments using Latent Semantic Indexing which contribute data points to the CUR theory